SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3051–3075 of 7282 papers

TitleStatusHype
Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference—0
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising—0
Analyzing and Improving Model Collapse in Rectified Flow Models—0
Denoising neural networks for magnetic resonance spectroscopy—0
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback—0
Analytical Discovery of Manifold with Machine Learning—0
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
Modified Weibull distribution for Biomedical signals denoising—0
Blind Image Restoration with Flow Based Priors—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Blind Image Denoising via Dependent Dirichlet Process Tree—0
Denoising Low-dose Images Using Deep Learning of Time Series Images—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Analysis of Fast Alternating Minimization for Structured Dictionary Learning—0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
Denoising Linear Models with Permuted Data—0
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified